The table below provides demographic information based on severity of injury
# level naming for categorical variables
df_demo$gender <- factor(df_demo$gender,
levels = c(1,2,3),
labels = c("Male", "Female", "Nonbinary"))
df_demo$work_current <- factor(df_demo$work_current,
levels = c(1,0),
labels = c("Yes", "No"))
df_demo$severity <- factor(df_demo$severity,
levels = c(2,3),
labels = c("Moderate", "Severe"))
df_demo$mech_injury <- factor(df_demo$mech_injury,
levels = c(1,2,3,4,5),
labels = c("Fall", "MVC", "Sports", "Violence", "Pedestrian struck"))
df_demo$income <- factor(df_demo$income,
levels = c(1,2,3),
labels = c("<52K", "52K-156K", ">156K"))
df_demo$marital_status <- factor(df_demo$marital_status,
levels = c(1, 2, 3, 4),
labels = c("Single", "Married", "Divorced", "Widowed"))
| Characteristic | N | Moderate, N = 191 | Severe, N = 281 | p-value2 |
|---|---|---|---|---|
| Age (years) | 47 | 51 (14) | 43 (15) | 0.062 |
| Time since TBI (years) | 47 | 7 (5) | 10 (8) | 0.17 |
| Gender | 47 |
|
|
0.17 |
| Male |
|
7 (37%) | 17 (61%) |
|
| Female |
|
11 (58%) | 11 (39%) |
|
| Nonbinary |
|
1 (5.3%) | 0 (0%) |
|
| Education (years) | 47 | 15.47 (2.04) | 15.00 (2.62) | 0.49 |
| Race/Ethnicity | 47 |
|
|
0.25 |
| Asian |
|
0 (0%) | 2 (7.1%) |
|
| Biracial |
|
2 (11%) | 0 (0%) |
|
| Black |
|
0 (0%) | 1 (3.6%) |
|
| Hispanic |
|
1 (5.3%) | 3 (11%) |
|
| White |
|
16 (84%) | 22 (79%) |
|
| Employment status | 47 |
|
|
0.62 |
| Yes |
|
9 (47%) | 10 (36%) |
|
| No |
|
10 (53%) | 18 (64%) |
|
| Annual household income | 47 |
|
|
0.83 |
| <52K |
|
6 (32%) | 10 (36%) |
|
| 52K-156K |
|
9 (47%) | 14 (50%) |
|
| >156K |
|
4 (21%) | 4 (14%) |
|
| Size household | 47 | 2.00 (1.05) | 2.25 (1.38) | 0.49 |
| Marital status | 47 |
|
|
0.089 |
| Single |
|
5 (26%) | 16 (57%) |
|
| Married |
|
11 (58%) | 8 (29%) |
|
| Divorced |
|
3 (16%) | 4 (14%) |
|
| Widowed |
|
0 (0%) | 0 (0%) |
|
| Substance use score | 47 | 4.16 (3.62) | 1.75 (1.94) | 0.014 |
| Cause of injury | 47 |
|
|
0.10 |
| Fall |
|
10 (53%) | 5 (18%) |
|
| MVC |
|
4 (21%) | 12 (43%) |
|
| Sports |
|
1 (5.3%) | 4 (14%) |
|
| Violence |
|
1 (5.3%) | 4 (14%) |
|
| Pedestrian struck |
|
3 (16%) | 3 (11%) |
|
| 1 Mean (SD); n (%) | ||||
| 2 Welch Two Sample t-test; Pearson’s Chi-squared test | ||||
ACS3 (activity re-engagement scores - outcome measure) by severity of injury
| Characteristic | N | Moderate, N = 191 | Severe, N = 281 | p-value2 |
|---|---|---|---|---|
| ACS Global Before | 47 | 72 (11) | 68 (10) | 0.23 |
| ACS Global Current | 47 | 54 (17) | 51 (13) | 0.53 |
| Global Retained (%) | 47 | 75 (19) | 75 (16) | 0.92 |
| ACS IADL Before | 47 | 22.16 (2.27) | 21.04 (3.25) | 0.17 |
| ACS IADL Current | 47 | 17.9 (4.9) | 16.7 (4.4) | 0.37 |
| IADL Retained (%) | 47 | 81 (19) | 80 (18) | 0.87 |
| ACS Leisure Before | 47 | 22.5 (5.8) | 21.0 (4.5) | 0.36 |
| ACS Leisure Current | 47 | 18.0 (6.0) | 16.3 (5.1) | 0.33 |
| Leisure Retained (%) | 47 | 82 (22) | 78 (18) | 0.58 |
| ACS Fitness Before | 47 | 13.2 (4.4) | 13.0 (4.5) | 0.89 |
| ACS Fitness Current | 47 | 8.3 (4.7) | 8.3 (3.3) |
0.99 |
| Fitness Retained (%) | 47 | 64 (32) | 68 (35) | 0.65 |
| ACS Social Before | 47 | 13.95 (1.22) | 12.93 (1.54) | 0.015 |
| ACS Social Current | 47 | 9.68 (3.08) | 9.71 (2.64) | 0.97 |
| Social Retained (%) | 47 | 69 (19) | 75 (20) | 0.27 |
| 1 Mean (SD) | ||||
| 2 Welch Two Sample t-test | ||||
Below is the ttest for the specific t and p value for the difference between ACS3 previous social score, which was significantly different.
##
## Welch Two Sample t-test
##
## data: df_mod$acss_prev and df_severe$acss_prev
## t = 2.5214, df = 43.755, p-value = 0.01541
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.2043487 1.8332453
## sample estimates:
## mean of x mean of y
## 13.94737 12.92857
Comparison of self-regulation scores by severity
| Characteristic | N | Moderate, N = 191 | Severe, N = 281 | p-value2 |
|---|---|---|---|---|
| Executive function | 47 | 41 (10) | 43 (11) | 0.67 |
| Disinhibition | 47 | 32.3 (6.1) | 33.6 (6.5) | 0.48 |
| Apathy | 47 | 33 (8) | 34 (9) | 0.57 |
| Total FrSBe Score | 47 | 106 (19) | 110 (22) | 0.50 |
| 1 Mean (SD) | ||||
| 2 Welch Two Sample t-test | ||||
Comparison of subscales of TBI QOL measure by severity of injury
| Characteristic | N | Moderate, N = 191 | Severe, N = 281 | p-value2 |
|---|---|---|---|---|
| Participation SRA | 47 | 47 (7) | 46 (6) | 0.44 |
| Anger | 47 | 53 (9) | 50 (10) | 0.34 |
| Anxiety | 47 | 58 (7) | 54 (10) | 0.14 |
| Communication | 47 | 46 (9) | 46 (10) | 0.85 |
| Depression | 47 | 55 (8) | 53 (11) | 0.60 |
| Dyscontrol | 47 | 52 (8) | 50 (9) | 0.53 |
| EF | 47 | 34.9 (4.8) | 35.8 (6.8) | 0.59 |
| Fatigue | 47 | 57 (8) | 54 (9) | 0.32 |
| Gen Cognition | 47 | 36 (8) | 36 (9) | 0.77 |
| Headache | 47 | 51 (9) | 49 (9) | 0.48 |
| Mobility | 47 | 47 (10) | 44 (7) | 0.25 |
| Pain | 47 | 58 (11) | 54 (10) | 0.24 |
| Positive Effect | 47 | 50 (6) | 50 (8) | 0.87 |
| Resilience | 47 | 49 (6) | 48 (9) | 0.54 |
| Satisfaction SRA | 47 | 46 (6) | 45 (6) | 0.50 |
| Self esteem | 47 | 47 (11) | 48 (11) | 0.74 |
| Stigma | 46 | 50 (8) | 52 (7) | 0.59 |
| Upper Extremity | 47 | 47 (9) | 42 (8) | 0.063 |
| 1 Mean (SD) | ||||
| 2 Welch Two Sample t-test | ||||
Comparison of composite scores for TBI QOL by severity of injury. Composite scores were calculated using:
Tyner, C. E., Boulton, A. J., Sherer, M., Kisala, P. A., Glutting, J. J., & Tulsky, D. S. (2020). Development of Composite Scores for the TBI-QOL. Arch Phys Med Rehabil, 101(1), 43-53. https://doi.org/10.1016/j.apmr.2018.05.036
| Characteristic | N | Moderate, N = 191 | Severe, N = 281 | p-value2 |
|---|---|---|---|---|
| Physical Health Index | 47 | 91 (14) | 96 (14) | 0.22 |
| Emotional Health Index | 47 | 97 (12) | 101 (15) | 0.29 |
| Cognitive Health Index | 47 | 93 (13) | 95 (16) | 0.77 |
| Social Health Index | 47 | 94 (12) | 91 (12) | 0.41 |
| Global Health Index | 47 | 93 (13) | 95 (14) | 0.55 |
| 1 Mean (SD) | ||||
| 2 Welch Two Sample t-test | ||||
Table 2 in dissertation
This table compares only the Personal and Environmental Protective factors and self-regulation outlined in the dissertation. Note that the Cognitive Health Composite score was not used as it includes executive functioning, which in this paper is considered a self-regulatory process. Therefore, general cognitive functioning was used which assesses memory and concentration.
| Characteristic | N | Moderate, N = 191 | Severe, N = 281 | p-value2 |
|---|---|---|---|---|
| Physical Health Index | 47 | 91 (14) | 96 (14) | 0.22 |
| Emotional Health Index | 47 | 97 (12) | 101 (15) | 0.29 |
| General Cognition | 47 | 36 (8) | 36 (9) | 0.77 |
| Extraversion | 47 | 7.16 (2.50) | 6.89 (2.33) | 0.72 |
| Agreeable | 47 | 7.11 (1.94) | 7.11 (2.08) |
0.99 |
| Consciousness | 47 | 8.16 (1.54) | 7.75 (1.94) | 0.43 |
| Neuroticism | 47 | 6.47 (2.20) | 6.21 (2.63) | 0.72 |
| Openness | 47 | 8.53 (2.09) | 7.25 (1.94) | 0.041 |
| Annual household income | 47 |
|
|
0.83 |
| <52K |
|
6 (32%) | 10 (36%) |
|
| 52K-156K |
|
9 (47%) | 14 (50%) |
|
| >156K |
|
4 (21%) | 4 (14%) |
|
| Marital status | 47 |
|
|
0.089 |
| Single |
|
5 (26%) | 16 (57%) |
|
| Married |
|
11 (58%) | 8 (29%) |
|
| Divorced |
|
3 (16%) | 4 (14%) |
|
| Widowed |
|
0 (0%) | 0 (0%) |
|
| Social Support | 47 | 84 (10) | 76 (11) | 0.014 |
| Executive function | 47 | 41 (10) | 43 (11) | 0.67 |
| Disinhibition | 47 | 32.3 (6.1) | 33.6 (6.5) | 0.48 |
| Apathy | 47 | 33 (8) | 34 (9) | 0.57 |
| Total score | 47 | 106 (19) | 110 (22) | 0.50 |
| 1 Mean (SD); n (%) | ||||
| 2 Welch Two Sample t-test; Pearson’s Chi-squared test | ||||
##
## Welch Two Sample t-test
##
## data: df_mod$bfi_openness and df_severe$bfi_openness
## t = 2.115, df = 36.744, p-value = 0.04127
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.05332003 2.49931155
## sample estimates:
## mean of x mean of y
## 8.526316 7.250000
Below is the t-test for SPS total, which was significantly different between severity of injury
##
## Welch Two Sample t-test
##
## data: df_mod$spstotal and df_severe$spstotal
## t = 2.5765, df = 41.069, p-value = 0.01367
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.737685 14.337503
## sample estimates:
## mean of x mean of y
## 83.89474 75.85714
Descriptive statistics for each variable of interest for the data set including mean, median, SD, and IQR, kurtosis and se
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| record_id* | 1 | 47 | 24.00 | 13.71 | 24.0 | 24.00 | 17.79 | 1.0 | 47.0 | 46.0 | 0.00 | -1.28 | 2.00 |
| age_current | 2 | 47 | 46.51 | 14.77 | 45.0 | 46.44 | 17.79 | 21.0 | 72.0 | 51.0 | 0.15 | -1.16 | 2.16 |
| age_injury | 3 | 47 | 37.62 | 15.07 | 34.0 | 36.87 | 16.31 | 18.0 | 66.0 | 48.0 | 0.38 | -1.25 | 2.20 |
| time_injury | 4 | 47 | 8.96 | 7.08 | 7.0 | 8.06 | 5.93 | 1.0 | 30.0 | 29.0 | 1.16 | 0.79 | 1.03 |
| gender | 5 | 47 | 1.51 | 0.55 | 1.0 | 1.49 | 0.00 | 1.0 | 3.0 | 2.0 | 0.35 | -1.14 | 0.08 |
| race* | 6 | 47 | 4.57 | 1.04 | 5.0 | 4.85 | 0.00 | 1.0 | 5.0 | 4.0 | -2.43 | 4.76 | 0.15 |
| edu | 7 | 47 | 15.19 | 2.39 | 16.0 | 15.21 | 2.97 | 10.0 | 20.0 | 10.0 | -0.15 | -0.76 | 0.35 |
| work_current | 8 | 47 | 0.40 | 0.50 | 0.0 | 0.38 | 0.00 | 0.0 | 1.0 | 1.0 | 0.38 | -1.90 | 0.07 |
| hours_work | 9 | 19 | 30.50 | 13.04 | 40.0 | 31.50 | 0.00 | 4.0 | 40.0 | 36.0 | -0.74 | -1.19 | 2.99 |
| occ_years_pos | 10 | 18 | 7.43 | 9.21 | 3.5 | 6.47 | 5.04 | 0.1 | 30.0 | 29.9 | 1.47 | 1.14 | 2.17 |
| diff_occ | 11 | 19 | 0.53 | 0.51 | 1.0 | 0.53 | 0.00 | 0.0 | 1.0 | 1.0 | -0.10 | -2.09 | 0.12 |
| no_occ_stat | 12 | 28 | 3.18 | 0.72 | 3.0 | 3.12 | 0.00 | 2.0 | 5.0 | 3.0 | 1.45 | 1.89 | 0.14 |
| income | 13 | 47 | 1.83 | 0.70 | 2.0 | 1.79 | 1.48 | 1.0 | 3.0 | 2.0 | 0.23 | -1.01 | 0.10 |
| house_size | 14 | 47 | 2.15 | 1.25 | 2.0 | 1.97 | 1.48 | 1.0 | 7.0 | 6.0 | 1.55 | 3.07 | 0.18 |
| children | 15 | 47 | 0.43 | 0.50 | 0.0 | 0.41 | 0.00 | 0.0 | 1.0 | 1.0 | 0.29 | -1.96 | 0.07 |
| num_child | 16 | 20 | 2.00 | 1.26 | 2.0 | 1.75 | 1.48 | 1.0 | 5.0 | 4.0 | 1.21 | 0.49 | 0.28 |
| severity | 17 | 47 | 2.60 | 0.50 | 3.0 | 2.62 | 0.00 | 2.0 | 3.0 | 1.0 | -0.38 | -1.90 | 0.07 |
| mech_injury | 18 | 47 | 2.38 | 1.38 | 2.0 | 2.26 | 1.48 | 1.0 | 5.0 | 4.0 | 0.73 | -0.80 | 0.20 |
| mech_injury_other* | 19 | 6 | 1.17 | 0.41 | 1.0 | 1.17 | 0.00 | 1.0 | 2.0 | 1.0 | 1.36 | -0.08 | 0.17 |
| substance | 20 | 47 | 2.72 | 2.96 | 2.0 | 2.28 | 2.97 | 0.0 | 13.0 | 13.0 | 1.31 | 1.77 | 0.43 |
| acsg_prev | 21 | 47 | 69.55 | 10.21 | 68.0 | 69.38 | 10.38 | 48.0 | 93.0 | 45.0 | 0.23 | -0.67 | 1.49 |
| acsg_curr | 22 | 47 | 52.16 | 14.35 | 49.0 | 51.28 | 14.53 | 29.5 | 85.5 | 56.0 | 0.53 | -0.44 | 2.09 |
| acsg_retain | 23 | 47 | 75.21 | 17.11 | 74.0 | 75.46 | 14.83 | 40.0 | 107.0 | 67.0 | -0.14 | -0.48 | 2.50 |
| acsi_prev | 24 | 47 | 21.49 | 2.92 | 21.0 | 21.59 | 2.97 | 12.0 | 26.0 | 14.0 | -0.53 | 0.62 | 0.43 |
| acsi_curr | 25 | 47 | 17.17 | 4.60 | 17.0 | 16.87 | 5.93 | 10.5 | 26.0 | 15.5 | 0.45 | -1.05 | 0.67 |
| acsi_retain | 26 | 47 | 80.17 | 18.37 | 82.0 | 80.64 | 22.24 | 42.0 | 110.0 | 68.0 | -0.16 | -1.13 | 2.68 |
| acsl_prev | 27 | 47 | 21.60 | 5.05 | 21.0 | 21.64 | 5.93 | 11.0 | 32.0 | 21.0 | -0.04 | -0.82 | 0.74 |
| acsl_curr | 28 | 47 | 17.01 | 5.46 | 16.0 | 16.76 | 5.93 | 8.5 | 28.0 | 19.5 | 0.42 | -0.99 | 0.80 |
| acsl_retain | 29 | 47 | 79.57 | 19.28 | 78.0 | 79.90 | 17.79 | 38.0 | 122.0 | 84.0 | -0.11 | -0.49 | 2.81 |
| acsf_prev | 30 | 47 | 13.11 | 4.38 | 13.0 | 13.23 | 4.45 | 4.0 | 20.0 | 16.0 | -0.24 | -0.96 | 0.64 |
| acsf_curr | 31 | 47 | 8.27 | 3.91 | 8.0 | 8.08 | 3.71 | 1.0 | 18.0 | 17.0 | 0.35 | -0.09 | 0.57 |
| acsf_retain | 32 | 47 | 66.19 | 33.29 | 63.0 | 62.90 | 23.72 | 9.0 | 200.0 | 191.0 | 1.53 | 3.93 | 4.86 |
| acss_prev | 33 | 47 | 13.34 | 1.49 | 14.0 | 13.44 | 1.48 | 9.0 | 17.0 | 8.0 | -0.46 | 0.51 | 0.22 |
| acss_curr | 34 | 47 | 9.70 | 2.79 | 10.0 | 9.77 | 2.97 | 4.5 | 15.0 | 10.5 | -0.26 | -0.70 | 0.41 |
| acss_retain | 35 | 47 | 72.79 | 19.57 | 73.0 | 73.51 | 19.27 | 32.0 | 109.0 | 77.0 | -0.24 | -0.82 | 2.85 |
| activity_card_sort_complete | 36 | 47 | 2.00 | 0.00 | 2.0 | 2.00 | 0.00 | 2.0 | 2.0 | 0.0 | NaN | NaN | 0.00 |
| spstotal | 37 | 47 | 79.11 | 11.29 | 82.0 | 79.56 | 14.83 | 55.0 | 96.0 | 41.0 | -0.28 | -1.23 | 1.65 |
| bfi_extraversion | 38 | 47 | 7.00 | 2.38 | 7.0 | 7.13 | 2.97 | 2.0 | 10.0 | 8.0 | -0.26 | -1.03 | 0.35 |
| bfi_agreeable | 39 | 47 | 7.11 | 2.00 | 7.0 | 7.18 | 2.97 | 3.0 | 10.0 | 7.0 | -0.37 | -0.92 | 0.29 |
| bfi_consciousness | 40 | 47 | 7.91 | 1.78 | 8.0 | 8.08 | 1.48 | 3.0 | 10.0 | 7.0 | -0.71 | -0.32 | 0.26 |
| bfi_neuroticism | 41 | 47 | 6.32 | 2.44 | 6.0 | 6.36 | 2.97 | 2.0 | 10.0 | 8.0 | -0.06 | -1.14 | 0.36 |
| bfi_openness | 42 | 47 | 7.77 | 2.08 | 8.0 | 7.95 | 2.97 | 2.0 | 10.0 | 8.0 | -0.51 | -0.56 | 0.30 |
| frsbe_exec | 43 | 47 | 42.19 | 10.29 | 43.0 | 41.97 | 10.38 | 24.0 | 63.0 | 39.0 | 0.10 | -0.89 | 1.50 |
| frsbe_apathy | 44 | 47 | 33.36 | 8.53 | 32.0 | 32.92 | 10.38 | 18.0 | 53.0 | 35.0 | 0.41 | -0.70 | 1.24 |
| frsbe_disinhib | 45 | 47 | 33.06 | 6.35 | 32.0 | 32.82 | 7.41 | 21.0 | 49.0 | 28.0 | 0.34 | -0.48 | 0.93 |
| frsbe_total | 46 | 47 | 108.62 | 20.68 | 109.0 | 108.18 | 23.72 | 72.0 | 150.0 | 78.0 | 0.20 | -0.95 | 3.02 |
| frsbe_complete | 47 | 47 | 2.00 | 0.00 | 2.0 | 2.00 | 0.00 | 2.0 | 2.0 | 0.0 | NaN | NaN | 0.00 |
| tbiqol_part_sra_tscore | 48 | 47 | 46.25 | 6.66 | 45.9 | 45.72 | 5.78 | 32.1 | 64.1 | 32.0 | 0.87 | 1.17 | 0.97 |
| tbiqol_anger_tscore | 49 | 47 | 51.32 | 9.92 | 51.7 | 51.27 | 11.56 | 33.1 | 69.9 | 36.8 | 0.03 | -1.05 | 1.45 |
| tbiqol_anxiety_tscore | 50 | 47 | 55.69 | 9.14 | 56.8 | 55.95 | 10.08 | 36.1 | 73.0 | 36.9 | -0.25 | -0.68 | 1.33 |
| tbiqol_comm_tscore | 51 | 47 | 46.04 | 9.56 | 44.9 | 45.89 | 9.49 | 29.2 | 65.5 | 36.3 | 0.19 | -0.88 | 1.39 |
| tbiqol_depression_tscore | 52 | 47 | 54.03 | 9.69 | 53.9 | 54.19 | 10.08 | 33.6 | 74.0 | 40.4 | -0.11 | -0.68 | 1.41 |
| tbiqol_dyscontrol_tscore | 53 | 47 | 50.99 | 8.21 | 52.4 | 51.42 | 7.86 | 33.2 | 66.8 | 33.6 | -0.45 | -0.45 | 1.20 |
| tbiqol_execfunc_tscore | 54 | 47 | 35.44 | 6.00 | 34.3 | 35.17 | 5.19 | 24.3 | 50.8 | 26.5 | 0.41 | -0.46 | 0.88 |
| tbiqol_fatigue_tscore | 55 | 47 | 55.09 | 8.52 | 54.7 | 55.18 | 8.15 | 37.9 | 72.5 | 34.6 | -0.02 | -0.67 | 1.24 |
| tbiqol_genconcern_tscore | 56 | 47 | 36.09 | 8.72 | 35.7 | 35.91 | 8.60 | 19.7 | 53.8 | 34.1 | 0.21 | -0.67 | 1.27 |
| tbiqol_grief_tscore | 57 | 47 | 52.78 | 9.45 | 53.9 | 53.35 | 8.01 | 30.7 | 70.3 | 39.6 | -0.62 | -0.13 | 1.38 |
| tbiqol_headache_tscore | 58 | 47 | 49.63 | 9.22 | 49.5 | 49.23 | 13.64 | 38.5 | 67.1 | 28.6 | 0.10 | -1.36 | 1.34 |
| tbiqol_mobility_tscore | 59 | 47 | 45.56 | 8.70 | 44.1 | 45.12 | 8.01 | 31.5 | 63.6 | 32.1 | 0.49 | -0.68 | 1.27 |
| tbiqol_pain_tscore | 60 | 47 | 55.41 | 10.67 | 57.4 | 55.42 | 11.12 | 38.4 | 74.8 | 36.4 | -0.28 | -1.06 | 1.56 |
| tbiqol_posaffect_tscore | 61 | 47 | 49.87 | 7.51 | 49.3 | 49.72 | 7.86 | 35.4 | 68.9 | 33.5 | 0.22 | -0.56 | 1.10 |
| tbiqol_resilience_tscore | 62 | 47 | 48.41 | 8.01 | 49.1 | 48.09 | 7.41 | 33.4 | 73.6 | 40.2 | 0.46 | 0.51 | 1.17 |
| tbiqol_selfesteem_tscore | 63 | 47 | 47.93 | 10.65 | 48.0 | 47.94 | 10.82 | 28.4 | 66.0 | 37.6 | 0.02 | -0.95 | 1.55 |
| tbiqol_satissra_tscore | 64 | 47 | 45.25 | 6.19 | 45.1 | 44.82 | 4.60 | 34.7 | 63.2 | 28.5 | 0.88 | 1.27 | 0.90 |
| tbiqol_stigma_tscore | 65 | 46 | 51.04 | 7.32 | 52.0 | 51.67 | 5.63 | 33.5 | 62.3 | 28.8 | -0.76 | -0.05 | 1.08 |
| tbiqol_ue_tscore | 66 | 47 | 43.97 | 8.55 | 42.5 | 43.83 | 8.30 | 27.9 | 58.1 | 30.2 | 0.43 | -0.90 | 1.25 |
| marital_status | 67 | 47 | 1.70 | 0.72 | 2.0 | 1.64 | 1.48 | 1.0 | 3.0 | 2.0 | 0.49 | -1.01 | 0.10 |
| phys_health | 68 | 47 | 110.50 | 17.08 | 110.9 | 110.54 | 19.87 | 82.6 | 143.3 | 60.7 | -0.10 | -1.10 | 2.49 |
| phys_health_index | 69 | 47 | 94.36 | 13.77 | 95.0 | 94.49 | 16.31 | 64.0 | 117.0 | 53.0 | -0.08 | -0.97 | 2.01 |
| emo_health | 70 | 47 | 161.04 | 25.49 | 158.9 | 161.67 | 31.73 | 115.1 | 201.3 | 86.2 | -0.13 | -1.25 | 3.72 |
| emo_health_index | 71 | 47 | 99.15 | 14.00 | 101.0 | 98.90 | 17.79 | 77.0 | 123.0 | 46.0 | 0.08 | -1.33 | 2.04 |
| cog_health | 72 | 47 | 71.53 | 14.22 | 70.0 | 71.19 | 13.94 | 44.0 | 104.6 | 60.6 | 0.30 | -0.56 | 2.07 |
| cog_health_index | 73 | 47 | 94.00 | 14.59 | 92.0 | 94.00 | 14.83 | 62.0 | 123.0 | 61.0 | 0.04 | -0.71 | 2.13 |
| soc_health | 74 | 47 | 91.50 | 11.93 | 90.0 | 90.83 | 10.38 | 69.6 | 127.3 | 57.7 | 0.69 | 0.67 | 1.74 |
| soc_health_index | 75 | 47 | 92.64 | 12.10 | 92.0 | 92.87 | 10.38 | 64.0 | 122.0 | 58.0 | -0.16 | 0.34 | 1.76 |
| glob_health | 76 | 47 | 380.15 | 45.21 | 380.0 | 379.26 | 54.86 | 303.0 | 468.0 | 165.0 | 0.11 | -1.11 | 6.59 |
| glob_health_index | 77 | 47 | 94.23 | 13.32 | 95.0 | 93.95 | 14.83 | 71.0 | 120.0 | 49.0 | 0.06 | -1.03 | 1.94 |
Outcome variable: ACS3 for all scores mean(sd)
| Characteristic | N = 471 |
|---|---|
| ACS Global Before | 70 (10) |
| ACS Global Current | 52 (14) |
| Global Retained (%) | 75 (17) |
| ACS IADL Before | 21.49 (2.92) |
| ACS IADL Current | 17.2 (4.6) |
| IADL Retained (%) | 80 (18) |
| ACS Leisure Before | 21.6 (5.1) |
| ACS Leisure Current | 17.0 (5.5) |
| Leisure Retained (%) | 80 (19) |
| ACS Fitness Before | 13.1 (4.4) |
| ACS Fitness Current | 8.3 (3.9) |
| Fitness Retained (%) | 66 (33) |
| ACS Social Before | 13.34 (1.49) |
| ACS Social Current | 9.70 (2.79) |
| Social Retained (%) | 73 (20) |
| 1 Mean (SD) | |
Correlation of all variables of interest with TBI QOL subscores. While too small to read in HTML print out, nice reference during analysis
This is pretty hard to read, so the following matrices break it down into smaller parts
Correlation matrix of PPF only (using composite TBIQOL Scores)
Matrix with heat map for all included variables in dissertation. Figure 4 in dissertation
Below is the breakdown of all TBIQOL sub scores with the ACS3. While not included in this study, helpful for discussion and future publications.
TBI QOL subscales with the FrSBe. I think this could be really interested as a future paper given how some subscales of the TBI QOL overlap with FrSBe (Exec functioning, anger, dyscontrol)
Research Question 1 1. What is the relationship between protective factors and self-regulation with resiliency-related outcomes such as re-engagement in meaningful activities? a. To what extent do protective factors and self-regulation predict resiliency-related outcomes in the TBI population? Hypothesis: Higher self-regulation will be associated with better resiliency-related outcomes b. To what extent does self-regulation mediate or moderate the influence of protective factors on resiliency-related outcomes after TBI? Hypothesis: Self-regulation will impact the relationship between protective factors and resiliency-related outcomes
First, we’ll look at the hierarchical linear model as outlined in Chapter 3. Then, to dive deeper, a “post hoc” analysis of each subscale of the ACS and use AIC to determine model of best fit.
In this section, we’ll do the original hierarchical model with protective and environmental protective factors in the first step and then total self-regulation score added for the second. *note that the cognitive composite score is not included as it includes exec functioning, which in this paper is seen as a self-regulatory process. therefore, gen concerns (memory and concentration) is used as cognitive protective factor
step1 <- lm(acsg_retain~age_current, data=df)
summary(step1)
##
## Call:
## lm(formula = acsg_retain ~ age_current, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.363 -10.024 -0.443 9.097 35.486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 86.4267 8.2323 10.498 1.11e-13 ***
## age_current -0.2411 0.1689 -1.428 0.16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.92 on 45 degrees of freedom
## Multiple R-squared: 0.04334, Adjusted R-squared: 0.02208
## F-statistic: 2.039 on 1 and 45 DF, p-value: 0.1602
step2<- lm(acsg_retain~age_current+phys_health_index+tbiqol_genconcern_tscore+emo_health_index+ spstotal, data=df)
summary(step2)
##
## Call:
## lm(formula = acsg_retain ~ age_current + phys_health_index +
## tbiqol_genconcern_tscore + emo_health_index + spstotal, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.849 -9.614 -1.017 10.909 33.458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.58124 25.56886 0.961 0.3420
## age_current -0.01410 0.16700 -0.084 0.9331
## phys_health_index 0.14439 0.23883 0.605 0.5488
## tbiqol_genconcern_tscore 0.79509 0.38979 2.040 0.0478 *
## emo_health_index -0.09938 0.23424 -0.424 0.6736
## spstotal 0.23788 0.25473 0.934 0.3558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.38 on 41 degrees of freedom
## Multiple R-squared: 0.2803, Adjusted R-squared: 0.1926
## F-statistic: 3.194 on 5 and 41 DF, p-value: 0.01586
#Nested Model Comparison
anova(step1, step2)
## Analysis of Variance Table
##
## Model 1: acsg_retain ~ age_current
## Model 2: acsg_retain ~ age_current + phys_health_index + tbiqol_genconcern_tscore +
## emo_health_index + spstotal
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 45 12884.1
## 2 41 9692.2 4 3191.9 3.3756 0.01776 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#change in R-squared
summary(step2)$r.squared - summary(step1)$r.squared
## [1] 0.2370005
step3<- lm(acsg_retain~age_current+phys_health_index+tbiqol_genconcern_tscore+emo_health_index+ spstotal+frsbe_total, data=df)
summary(step3)
##
## Call:
## lm(formula = acsg_retain ~ age_current + phys_health_index +
## tbiqol_genconcern_tscore + emo_health_index + spstotal +
## frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.554 -9.303 0.821 9.139 33.358
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 51.19175 40.65439 1.259 0.215
## age_current -0.03531 0.16946 -0.208 0.836
## phys_health_index 0.14505 0.23967 0.605 0.548
## tbiqol_genconcern_tscore 0.67895 0.41467 1.637 0.109
## emo_health_index -0.13345 0.23851 -0.560 0.579
## spstotal 0.17925 0.26491 0.677 0.503
## frsbe_total -0.12408 0.14704 -0.844 0.404
##
## Residual standard error: 15.43 on 40 degrees of freedom
## Multiple R-squared: 0.2929, Adjusted R-squared: 0.1869
## F-statistic: 2.762 on 6 and 40 DF, p-value: 0.02427
#Nested Model Comparison
anova(step2, step3)
## Analysis of Variance Table
##
## Model 1: acsg_retain ~ age_current + phys_health_index + tbiqol_genconcern_tscore +
## emo_health_index + spstotal
## Model 2: acsg_retain ~ age_current + phys_health_index + tbiqol_genconcern_tscore +
## emo_health_index + spstotal + frsbe_total
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 41 9692.2
## 2 40 9522.7 1 169.53 0.7121 0.4038
#change in R-squared
summary(step3)$r.squared - summary(step2)$r.squared
## [1] 0.01258785
Test for multicollinearity
## age_current phys_health_index tbiqol_genconcern_tscore
## 1.211297 2.104684 2.526230
## emo_health_index spstotal frsbe_total
## 2.153464 1.729262 1.786663
As we have a smaller n and need to be parsimonious with the variables we use in the regression model, we’ll look at several models based on correlations (higher correlations added to the models) and then calculate the AIC. The lower AIC, the better the fit and that model will be used
Note that all assumptions were tested for each model and were met
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## model6 7 405.58 0.00 0.33 0.33 -194.35
## model4 7 405.83 0.25 0.29 0.61 -194.48
## model3 8 407.08 1.50 0.15 0.77 -193.64
## model5 8 408.23 2.65 0.09 0.85 -194.22
## model2 8 408.54 2.96 0.07 0.93 -194.37
## model1 8 408.60 3.02 0.07 1.00 -194.41
##
## Call:
## lm(formula = acsi_retain ~ tbiqol_mobility_tscore + tbiqol_genconcern_tscore +
## tbiqol_comm_tscore + tbiqol_ue_tscore + frsbe_apathy, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.856 -10.388 -0.861 12.723 29.554
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.5842 22.9859 1.548 0.1293
## tbiqol_mobility_tscore 0.5331 0.4564 1.168 0.2495
## tbiqol_genconcern_tscore 0.6523 0.3581 1.822 0.0758 .
## tbiqol_comm_tscore -0.2707 0.3880 -0.698 0.4893
## tbiqol_ue_tscore 0.3729 0.4641 0.803 0.4263
## frsbe_apathy -0.2153 0.3294 -0.654 0.5170
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.19 on 41 degrees of freedom
## Multiple R-squared: 0.3072, Adjusted R-squared: 0.2228
## F-statistic: 3.637 on 5 and 41 DF, p-value: 0.008165
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## model7 6 409.52 0.00 0.34 0.34 -197.71
## model3 6 410.03 0.51 0.26 0.60 -197.97
## model6 6 410.45 0.93 0.21 0.81 -198.17
## model1 7 412.27 2.75 0.09 0.89 -197.70
## model4 7 412.64 3.12 0.07 0.97 -197.88
## model2 8 415.14 5.61 0.02 0.99 -197.67
## model5 7 415.84 6.32 0.01 1.00 -199.48
##
## Call:
## lm(formula = acsl_retain ~ tbiqol_mobility_tscore + tbiqol_genconcern_tscore +
## tbiqol_comm_tscore + frsbe_apathy, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.799 -10.757 -0.328 10.936 36.487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.2998 24.6323 1.595 0.1181
## tbiqol_mobility_tscore 0.6774 0.3671 1.845 0.0721 .
## tbiqol_genconcern_tscore 0.7580 0.3838 1.975 0.0549 .
## tbiqol_comm_tscore -0.2437 0.4005 -0.608 0.5462
## frsbe_apathy -0.2018 0.3492 -0.578 0.5665
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.35 on 42 degrees of freedom
## Multiple R-squared: 0.2601, Adjusted R-squared: 0.1896
## F-statistic: 3.691 on 4 and 42 DF, p-value: 0.01156
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## model8 7 459.79 0.00 0.46 0.46 -221.42
## model2 6 460.55 0.76 0.32 0.78 -223.20
## model7 8 462.71 2.93 0.11 0.89 -221.41
## model1 8 464.88 5.09 0.04 0.93 -222.49
## model5 8 464.95 5.17 0.04 0.96 -222.53
## model6 9 465.07 5.29 0.03 0.99 -221.04
## model4 6 469.04 9.26 0.00 1.00 -227.47
## model3 7 470.64 10.86 0.00 1.00 -226.88
##
## Call:
## lm(formula = acsf_retain ~ phys_health_index + tbiqol_genconcern_tscore +
## tbiqol_anxiety_tscore + tbiqol_stigma_tscore + income, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.951 -17.189 -7.207 8.118 123.427
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.9599 69.0538 0.680 0.5004
## phys_health_index 0.3362 0.4663 0.721 0.4751
## tbiqol_genconcern_tscore 0.2881 0.7446 0.387 0.7008
## tbiqol_anxiety_tscore -0.1669 0.7041 -0.237 0.8139
## tbiqol_stigma_tscore -0.7124 0.8671 -0.822 0.4162
## income 12.3356 6.8803 1.793 0.0806 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.96 on 40 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1986, Adjusted R-squared: 0.09848
## F-statistic: 1.983 on 5 and 40 DF, p-value: 0.1021
For moderation, looking at personal protective factors and environmental protective factors from original model
##
## Call:
## lm(formula = acsg_retain ~ phys_health_index * frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.175 -8.852 -1.446 10.181 35.091
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.685155 102.569609 0.562 0.577
## phys_health_index 0.469664 1.030243 0.456 0.651
## frsbe_total -0.117970 0.914296 -0.129 0.898
## phys_health_index:frsbe_total -0.001378 0.009340 -0.148 0.883
##
## Residual standard error: 15.64 on 43 degrees of freedom
## Multiple R-squared: 0.2189, Adjusted R-squared: 0.1644
## F-statistic: 4.018 on 3 and 43 DF, p-value: 0.01319
##
## Call:
## lm(formula = acsg_retain ~ tbiqol_genconcern_tscore * frsbe_total,
## data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.944 -9.354 1.289 7.825 32.664
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 51.480917 54.513263 0.944 0.350
## tbiqol_genconcern_tscore 1.063893 1.434613 0.742 0.462
## frsbe_total -0.051765 0.492276 -0.105 0.917
## tbiqol_genconcern_tscore:frsbe_total -0.002369 0.013757 -0.172 0.864
##
## Residual standard error: 15.02 on 43 degrees of freedom
## Multiple R-squared: 0.2801, Adjusted R-squared: 0.2298
## F-statistic: 5.575 on 3 and 43 DF, p-value: 0.00254
##
## Call:
## lm(formula = acsg_retain ~ emo_health_index * frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.4760 -7.4443 0.5405 9.4269 31.2846
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -32.783896 110.933886 -0.296 0.769
## emo_health_index 1.322256 1.037817 1.274 0.209
## frsbe_total 0.803242 0.960737 0.836 0.408
## emo_health_index:frsbe_total -0.010388 0.009178 -1.132 0.264
##
## Residual standard error: 15.83 on 43 degrees of freedom
## Multiple R-squared: 0.1996, Adjusted R-squared: 0.1438
## F-statistic: 3.574 on 3 and 43 DF, p-value: 0.02148
##
## Call:
## lm(formula = acsg_retain ~ spstotal * frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.313 -9.575 1.231 8.384 31.091
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.055e+01 1.136e+02 0.709 0.482
## spstotal 2.904e-01 1.410e+00 0.206 0.838
## frsbe_total -2.342e-01 1.025e+00 -0.228 0.820
## spstotal:frsbe_total -3.385e-04 1.306e-02 -0.026 0.979
##
## Residual standard error: 16 on 43 degrees of freedom
## Multiple R-squared: 0.1824, Adjusted R-squared: 0.1254
## F-statistic: 3.198 on 3 and 43 DF, p-value: 0.03269
There was no moderating effect of apathy on any of the predictors.
Here we look at mediation effect of the total FrSBe scores on the personal and environmental factors used in the post hoc model (mobility, general cog functioning, anxiety, depression, and social support)
How to read: ACME = indirect effect. ADE = direct effect. ACME + ADE = total effect.
# Initial Model
model1 <- lm(acsg_retain ~ phys_health_index, df) # Y ~ X, DV predicted by IV - no mediation considered - total effect
summary(model1)
##
## Call:
## lm(formula = acsg_retain ~ phys_health_index, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.509 -9.597 -2.509 11.759 34.771
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 31.4292 16.3825 1.918 0.06141 .
## phys_health_index 0.4640 0.1718 2.700 0.00973 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.05 on 45 degrees of freedom
## Multiple R-squared: 0.1394, Adjusted R-squared: 0.1203
## F-statistic: 7.292 on 1 and 45 DF, p-value: 0.009728
# Mediation paths
medmodel1 <- lm(frsbe_total ~ phys_health_index, df) # M ~ X, mediator predicted by X
outputmodel1 <- lm(acsg_retain ~ phys_health_index + frsbe_total, df) # Y ~ X + M, DV predicted by mediator, adjusting for IV
summary(medmodel1)
##
## Call:
## lm(formula = frsbe_total ~ phys_health_index, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.392 -15.118 -1.668 12.312 48.040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 162.592 19.735 8.239 1.55e-10 ***
## phys_health_index -0.572 0.207 -2.763 0.00826 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.33 on 45 degrees of freedom
## Multiple R-squared: 0.1451, Adjusted R-squared: 0.1261
## F-statistic: 7.636 on 1 and 45 DF, p-value: 0.00826
summary(outputmodel1)
##
## Call:
## lm(formula = acsg_retain ~ phys_health_index + frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.321 -8.737 -1.367 9.834 35.352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 72.3506 25.0043 2.894 0.0059 **
## phys_health_index 0.3200 0.1791 1.787 0.0808 .
## frsbe_total -0.2517 0.1193 -2.110 0.0405 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.47 on 44 degrees of freedom
## Multiple R-squared: 0.2185, Adjusted R-squared: 0.183
## F-statistic: 6.153 on 2 and 44 DF, p-value: 0.004404
# Mediation test
mediation <- mediate(medmodel1, # Mediator model
outputmodel1, # Outcome model
boot = T, # Ask for bootstrapped confidence intervals
treat="phys_health_index", # Name of the x variable
mediator="frsbe_total" # Name of the m variable
)
# if you don't want bootstrap, just delete 'sims' line and set boot = F
summary(mediation)
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.1440 0.0109 0.35 0.022 *
## ADE 0.3200 -0.0354 0.67 0.070 .
## Total Effect 0.4640 0.1259 0.84 0.008 **
## Prop. Mediated 0.3103 0.0246 1.12 0.030 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 47
##
##
## Simulations: 1000
plot(mediation)
There is a significant indirect effect and an insignificant direct
effect, indicating total mediation
## [1] "-0.57"
## [1] "-0.25"
#### Cognitive Health
##
## Call:
## lm(formula = acsg_retain ~ tbiqol_genconcern_tscore, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.004 -10.422 0.877 9.549 32.010
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.9504 9.3250 4.177 0.000134 ***
## tbiqol_genconcern_tscore 1.0047 0.2513 3.998 0.000235 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.86 on 45 degrees of freedom
## Multiple R-squared: 0.2621, Adjusted R-squared: 0.2457
## F-statistic: 15.99 on 1 and 45 DF, p-value: 0.0002345
##
## Call:
## lm(formula = frsbe_total ~ tbiqol_genconcern_tscore, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.149 -9.953 -0.151 11.214 40.165
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 157.8158 10.7369 14.699 < 2e-16 ***
## tbiqol_genconcern_tscore -1.3631 0.2893 -4.711 2.39e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.11 on 45 degrees of freedom
## Multiple R-squared: 0.3303, Adjusted R-squared: 0.3154
## F-statistic: 22.2 on 1 and 45 DF, p-value: 2.39e-05
##
## Call:
## lm(formula = acsg_retain ~ tbiqol_genconcern_tscore + frsbe_total,
## data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.123 -9.765 1.326 7.881 32.669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60.0145 22.4434 2.674 0.0105 *
## tbiqol_genconcern_tscore 0.8227 0.3068 2.681 0.0103 *
## frsbe_total -0.1335 0.1294 -1.032 0.3079
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.85 on 44 degrees of freedom
## Multiple R-squared: 0.2796, Adjusted R-squared: 0.2468
## F-statistic: 8.537 on 2 and 44 DF, p-value: 0.0007366
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.182 -0.118 0.54 0.238
## ADE 0.823 0.265 1.31 0.004 **
## Total Effect 1.005 0.532 1.49 <2e-16 ***
## Prop. Mediated 0.181 -0.131 0.61 0.238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 47
##
##
## Simulations: 1000
There is no significant indirect effect. No mediation
##
## Call:
## lm(formula = acsg_retain ~ emo_health_index, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.933 -9.024 -0.002 9.921 32.264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.7676 17.3514 2.177 0.0348 *
## emo_health_index 0.3777 0.1733 2.179 0.0346 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.45 on 45 degrees of freedom
## Multiple R-squared: 0.09544, Adjusted R-squared: 0.07534
## F-statistic: 4.748 on 1 and 45 DF, p-value: 0.03461
##
## Call:
## lm(formula = frsbe_total ~ emo_health_index, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.203 -12.814 -2.400 7.978 48.254
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 184.3468 18.8749 9.767 1.08e-12 ***
## emo_health_index -0.7638 0.1885 -4.051 0.000199 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.9 on 45 degrees of freedom
## Multiple R-squared: 0.2672, Adjusted R-squared: 0.251
## F-statistic: 16.41 on 1 and 45 DF, p-value: 0.0001989
##
## Call:
## lm(formula = acsg_retain ~ emo_health_index + frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.767 -8.418 -0.429 8.742 32.342
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 88.2618 29.5862 2.983 0.00464 **
## emo_health_index 0.1685 0.1955 0.862 0.39346
## frsbe_total -0.2739 0.1323 -2.070 0.04431 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.88 on 44 degrees of freedom
## Multiple R-squared: 0.1757, Adjusted R-squared: 0.1383
## F-statistic: 4.691 on 2 and 44 DF, p-value: 0.01423
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.2092 0.0171 0.43 0.040 *
## ADE 0.1685 -0.2411 0.58 0.370
## Total Effect 0.3777 0.0470 0.72 0.036 *
## Prop. Mediated 0.5540 -0.1432 2.81 0.076 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 47
##
##
## Simulations: 1000
There is no significant indirect effect indicating no mediation
## [1] "-0.76"
## [1] "-0.27"
#### SPS
##
## Call:
## lm(formula = acsg_retain ~ spstotal, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.673 -9.945 -0.095 8.838 32.349
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.4478 17.0229 2.082 0.0430 *
## spstotal 0.5027 0.2131 2.359 0.0227 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.32 on 45 degrees of freedom
## Multiple R-squared: 0.1101, Adjusted R-squared: 0.09029
## F-statistic: 5.566 on 1 and 45 DF, p-value: 0.02272
##
## Call:
## lm(formula = frsbe_total ~ spstotal, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -39.247 -14.035 -0.331 10.358 35.999
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 183.9965 18.6242 9.879 7.59e-13 ***
## spstotal -0.9529 0.2331 -4.088 0.000177 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.86 on 45 degrees of freedom
## Multiple R-squared: 0.2708, Adjusted R-squared: 0.2546
## F-statistic: 16.71 on 1 and 45 DF, p-value: 0.0001774
##
## Call:
## lm(formula = acsg_retain ~ spstotal + frsbe_total, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.358 -9.535 1.281 8.379 31.082
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 83.3909 29.3743 2.839 0.00683 **
## spstotal 0.2544 0.2419 1.052 0.29865
## frsbe_total -0.2606 0.1321 -1.973 0.05482 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.82 on 44 degrees of freedom
## Multiple R-squared: 0.1824, Adjusted R-squared: 0.1452
## F-statistic: 4.908 on 2 and 44 DF, p-value: 0.01191
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.2483 0.0383 0.56 0.018 *
## ADE 0.2544 -0.2218 0.70 0.304
## Total Effect 0.5027 0.0624 0.93 0.030 *
## Prop. Mediated 0.4939 0.0212 2.33 0.048 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 47
##
##
## Simulations: 1000
There is a significant indirect effect and insignificant direct effect
indicating full mediation
## [1] "-0.95"
## [1] "-0.26"
In this set of mediation analysis, we look specifically at apathy as a mediator for the ACS social re-engagement outcome
##
## Call:
## lm(formula = acss_retain ~ tbiqol_depression_tscore, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.638 -10.314 0.074 11.575 34.999
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 116.4764 15.1471 7.690 9.79e-10 ***
## tbiqol_depression_tscore -0.8087 0.2761 -2.929 0.00532 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.13 on 45 degrees of freedom
## Multiple R-squared: 0.1602, Adjusted R-squared: 0.1415
## F-statistic: 8.581 on 1 and 45 DF, p-value: 0.005317
##
## Call:
## lm(formula = frsbe_apathy ~ tbiqol_depression_tscore, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.1753 -6.2877 -0.3744 5.1128 20.9676
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.5876 6.3297 1.673 0.101327
## tbiqol_depression_tscore 0.4215 0.1154 3.654 0.000671 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.578 on 45 degrees of freedom
## Multiple R-squared: 0.2288, Adjusted R-squared: 0.2117
## F-statistic: 13.35 on 1 and 45 DF, p-value: 0.0006714
##
## Call:
## lm(formula = acss_retain ~ tbiqol_depression_tscore + frsbe_apathy,
## data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.30 -10.39 -1.80 16.06 26.89
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 124.6480 14.9436 8.341 1.31e-10 ***
## tbiqol_depression_tscore -0.4833 0.3009 -1.606 0.1154
## frsbe_apathy -0.7718 0.3415 -2.260 0.0288 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.36 on 44 degrees of freedom
## Multiple R-squared: 0.2475, Adjusted R-squared: 0.2133
## F-statistic: 7.236 on 2 and 44 DF, p-value: 0.001919
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.3254 -0.7283 -0.06 0.016 *
## ADE -0.4833 -1.1235 0.14 0.136
## Total Effect -0.8087 -1.3168 -0.32 <2e-16 ***
## Prop. Mediated 0.4023 0.0614 1.42 0.016 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 47
##
##
## Simulations: 1000
## [1] "0.42"
## [1] "-0.77"
##
## Call:
## lm(formula = acss_retain ~ tbiqol_fatigue_tscore, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.178 -10.715 1.675 14.500 40.993
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 129.0292 17.0969 7.547 1.59e-09 ***
## tbiqol_fatigue_tscore -1.0210 0.3068 -3.328 0.00175 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.73 on 45 degrees of freedom
## Multiple R-squared: 0.1975, Adjusted R-squared: 0.1797
## F-statistic: 11.07 on 1 and 45 DF, p-value: 0.001751
##
## Call:
## lm(formula = frsbe_apathy ~ tbiqol_fatigue_tscore, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.8790 -4.8855 -0.3758 4.5242 15.9984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.9308 7.1335 0.691 0.492975
## tbiqol_fatigue_tscore 0.5161 0.1280 4.032 0.000211 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.396 on 45 degrees of freedom
## Multiple R-squared: 0.2654, Adjusted R-squared: 0.2491
## F-statistic: 16.26 on 1 and 45 DF, p-value: 0.0002112
##
## Call:
## lm(formula = acss_retain ~ tbiqol_fatigue_tscore + frsbe_apathy,
## data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.271 -13.323 -0.586 13.963 40.058
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 132.4470 16.6389 7.960 4.6e-10 ***
## tbiqol_fatigue_tscore -0.6633 0.3465 -1.914 0.0621 .
## frsbe_apathy -0.6931 0.3459 -2.004 0.0512 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.16 on 44 degrees of freedom
## Multiple R-squared: 0.2646, Adjusted R-squared: 0.2312
## F-statistic: 7.917 on 2 and 44 DF, p-value: 0.001157
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.3578 -0.8107 -0.11 0.004 **
## ADE -0.6633 -1.3189 0.04 0.072 .
## Total Effect -1.0210 -1.6485 -0.44 <2e-16 ***
## Prop. Mediated 0.3504 0.0928 1.10 0.004 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 47
##
##
## Simulations: 1000
## [1] "0.52"
## [1] "-0.69"
##
## Call:
## lm(formula = acss_retain ~ tbiqol_genconcern_tscore, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.828 -13.186 4.512 11.990 26.980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 31.4994 10.6832 2.949 0.005049 **
## tbiqol_genconcern_tscore 1.1439 0.2879 3.974 0.000253 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.02 on 45 degrees of freedom
## Multiple R-squared: 0.2597, Adjusted R-squared: 0.2433
## F-statistic: 15.79 on 1 and 45 DF, p-value: 0.0002532
##
## Call:
## lm(formula = frsbe_apathy ~ tbiqol_genconcern_tscore, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.282 -5.537 0.206 4.894 14.257
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.7536 4.8739 10.003 5.15e-13 ***
## tbiqol_genconcern_tscore -0.4264 0.1313 -3.247 0.00221 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.767 on 45 degrees of freedom
## Multiple R-squared: 0.1898, Adjusted R-squared: 0.1718
## F-statistic: 10.54 on 1 and 45 DF, p-value: 0.002207
##
## Call:
## lm(formula = acss_retain ~ tbiqol_genconcern_tscore + frsbe_apathy,
## data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.659 -11.474 -0.009 13.358 28.851
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 63.0926 18.5305 3.405 0.00142 **
## tbiqol_genconcern_tscore 0.8676 0.3090 2.808 0.00741 **
## frsbe_apathy -0.6480 0.3157 -2.053 0.04607 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.45 on 44 degrees of freedom
## Multiple R-squared: 0.3244, Adjusted R-squared: 0.2937
## F-statistic: 10.57 on 2 and 44 DF, p-value: 0.0001789
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.27634 0.00381 0.59 0.048 *
## ADE 0.86757 0.32064 1.40 <2e-16 ***
## Total Effect 1.14391 0.63978 1.63 <2e-16 ***
## Prop. Mediated 0.24158 0.00720 0.59 0.048 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 47
##
##
## Simulations: 1000
## [1] "-0.43"
## [1] "-0.65"
To answer these questions, first look at descriptive statistics, then regression model with time since injury included, lastly, investigate what, if any, role time since injury has on protective factors and self-regulation
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## time_injury 1 3.25 7 8.96 11 30
## [1] 8.957447
## [1] 7.079387
First, looking just at the relationship between time since injury and re-engagement while controlling for age.
##
## Call:
## lm(formula = acsg_retain ~ age_current + time_injury, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.997 -9.010 -0.178 9.243 40.142
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 83.3414 8.1920 10.174 3.92e-13 ***
## age_current -0.2991 0.1675 -1.786 0.0810 .
## time_injury 0.6458 0.3495 1.848 0.0714 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.48 on 44 degrees of freedom
## Multiple R-squared: 0.1122, Adjusted R-squared: 0.07188
## F-statistic: 2.781 on 2 and 44 DF, p-value: 0.07288
Controlling for age, there is no significant relationship between time since injury and re-engagement
##
## Call:
## lm(formula = acsi_retain ~ age_current + time_injury, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.423 -15.564 0.161 16.149 30.936
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 86.0488 8.9658 9.597 2.34e-12 ***
## age_current -0.2444 0.1833 -1.333 0.189
## time_injury 0.6126 0.3825 1.602 0.116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.04 on 44 degrees of freedom
## Multiple R-squared: 0.077, Adjusted R-squared: 0.03504
## F-statistic: 1.835 on 2 and 44 DF, p-value: 0.1716
##
## Call:
## lm(formula = acsl_retain ~ age_current + time_injury, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.198 -10.286 -1.274 13.458 52.180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 89.1879 9.3269 9.562 2.61e-12 ***
## age_current -0.3264 0.1907 -1.712 0.0939 .
## time_injury 0.6217 0.3979 1.562 0.1254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.77 on 44 degrees of freedom
## Multiple R-squared: 0.09329, Adjusted R-squared: 0.05208
## F-statistic: 2.264 on 2 and 44 DF, p-value: 0.1159
##
## Call:
## lm(formula = acsf_retain ~ age_current + time_injury, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.303 -16.695 -4.872 10.540 127.414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 73.9164 16.3563 4.519 4.63e-05 ***
## age_current -0.3726 0.3344 -1.114 0.271
## time_injury 1.0721 0.6979 1.536 0.132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.91 on 44 degrees of freedom
## Multiple R-squared: 0.06517, Adjusted R-squared: 0.02267
## F-statistic: 1.534 on 2 and 44 DF, p-value: 0.2271
##
## Call:
## lm(formula = acsg_retain ~ age_current + phys_health_index +
## tbiqol_genconcern_tscore + emo_health_index + spstotal +
## frsbe_total + time_injury, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.214 -7.610 -2.886 7.239 33.115
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.0409 37.8837 1.215 0.2315
## age_current -0.1030 0.1597 -0.645 0.5227
## phys_health_index 0.1833 0.2235 0.820 0.4171
## tbiqol_genconcern_tscore 0.6243 0.3865 1.615 0.1143
## emo_health_index -0.1721 0.2224 -0.774 0.4437
## spstotal 0.2642 0.2486 1.063 0.2944
## frsbe_total -0.1579 0.1374 -1.149 0.2575
## time_injury 0.8321 0.3105 2.680 0.0107 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.36 on 39 degrees of freedom
## Multiple R-squared: 0.4029, Adjusted R-squared: 0.2957
## F-statistic: 3.76 on 7 and 39 DF, p-value: 0.003321
#Nested Model Comparison
anova(step3, step4)
## Analysis of Variance Table
##
## Model 1: acsg_retain ~ age_current + phys_health_index + tbiqol_genconcern_tscore +
## emo_health_index + spstotal + frsbe_total
## Model 2: acsg_retain ~ age_current + phys_health_index + tbiqol_genconcern_tscore +
## emo_health_index + spstotal + frsbe_total + time_injury
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 40 9522.7
## 2 39 8041.5 1 1481.2 7.1838 0.01072 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#change in R-squared
summary(step4)$r.squared - summary(step3)$r.squared
## [1] 0.1099826
Because there was no significant relationship between time since injury and outcomes BUT it was a significant predictor in the model, I seperated time since recovery into three groups to examine relationships further. Early = 6mo-3years Mid = 3 to 10 years Later = >10 years
1= everything <=3 and 3 is everything >=10
| Characteristic | Early, N = 121 | Mid, N = 141 | Later, N = 211 |
|---|---|---|---|
| Age (years) | 39 (14) | 50 (17) | 48 (13) |
| Employment status |
|
|
|
| 0 | 5 (42%) | 11 (79%) | 12 (57%) |
| 1 | 7 (58%) | 3 (21%) | 9 (43%) |
| Substance use score | 2.00 (1.71) | 3.21 (3.53) | 2.81 (3.16) |
| Severity of Injury |
|
|
|
| 2 | 5 (42%) | 7 (50%) | 7 (33%) |
| 3 | 7 (58%) | 7 (50%) | 14 (67%) |
| Global ACS | 68 (16) | 73 (18) | 80 (16) |
| Social ACS | 67 (20) | 69 (14) | 79 (22) |
| IADL ACS | 72 (19) | 79 (20) | 85 (16) |
| Leisure ACS | 71 (19) | 79 (21) | 85 (17) |
| Fitness ACS | 58 (22) | 61 (31) | 74 (40) |
| 1 Mean (SD); n (%) | |||
##
## Early Mid Later
## 12 14 21
We see the counts of # participants in each group
Global ACS3 scores (ie, global re-engagement scores)
## # A tibble: 3 × 4
## time_injury_ex count mean sd
## <fct> <int> <dbl> <dbl>
## 1 Early 12 68.3 16.0
## 2 Mid 14 73.4 18.3
## 3 Later 21 80.4 16.0
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 1177 588.5 2.107 0.134
## Residuals 44 12291 279.3
##
## Call:
## lm(formula = acsg_retain ~ age_current + time_injury_ex, data = df_time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.555 -10.753 0.697 9.204 38.098
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 81.7469 8.0927 10.101 6.35e-13 ***
## age_current -0.3425 0.1689 -2.028 0.0488 *
## time_injury_exMid 8.7584 6.6156 1.324 0.1925
## time_injury_exLater 15.1870 6.0465 2.512 0.0159 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.15 on 43 degrees of freedom
## Multiple R-squared: 0.1671, Adjusted R-squared: 0.1089
## F-statistic: 2.875 on 3 and 43 DF, p-value: 0.04708
Controlling for age, we see a significant relationship between time since injury and global re engagement- specifically between early and later recovery
IADL ACS3 scores (ie, IADL re-engagement scores)
## # A tibble: 3 × 4
## time_injury_ex count mean sd
## <fct> <int> <dbl> <dbl>
## 1 Early 12 72.2 18.8
## 2 Mid 14 79.1 20.2
## 3 Later 21 85.4 15.8
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 1364 682.1 2.121 0.132
## Residuals 44 14153 321.6
##
## Call:
## lm(formula = acsi_retain ~ age_current + time_injury_ex, data = df_time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.6008 -14.5223 -0.4196 14.5986 30.4952
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 83.9693 8.8190 9.521 3.74e-12 ***
## age_current -0.3013 0.1840 -1.637 0.1088
## time_injury_exMid 10.2623 7.2093 1.423 0.1618
## time_injury_exLater 16.0242 6.5891 2.432 0.0193 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.6 on 43 degrees of freedom
## Multiple R-squared: 0.1414, Adjusted R-squared: 0.08155
## F-statistic: 2.361 on 3 and 43 DF, p-value: 0.08459
We see a significant difference between early and late groups with IADl engagement when controlling for age
Leisure ACS3 scores (ie, leisure re-engagement scores)
## # A tibble: 3 × 4
## time_injury_ex count mean sd
## <fct> <int> <dbl> <dbl>
## 1 Early 12 71.2 19.3
## 2 Mid 14 78.8 21.0
## 3 Later 21 84.9 17.0
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 1426 713.2 2.003 0.147
## Residuals 44 15667 356.1
##
## Call:
## lm(formula = acsl_retain ~ age_current + time_injury_ex, data = df_time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.426 -13.860 2.239 10.586 49.506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 86.7213 9.1178 9.511 3.86e-12 ***
## age_current -0.3950 0.1903 -2.076 0.0439 *
## time_injury_exMid 11.8432 7.4536 1.589 0.1194
## time_injury_exLater 17.2281 6.8124 2.529 0.0152 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.2 on 43 degrees of freedom
## Multiple R-squared: 0.1669, Adjusted R-squared: 0.1088
## F-statistic: 2.872 on 3 and 43 DF, p-value: 0.0472
We see a significant difference between early and late groups with Leisure engagement when controlling for age
Fitness ACS3 scores (ie, fitness re-engagement scores)
## # A tibble: 3 × 4
## time_injury_ex count mean sd
## <fct> <int> <dbl> <dbl>
## 1 Early 12 58.1 21.7
## 2 Mid 14 61.4 30.6
## 3 Later 21 74.0 39.5
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 2412 1206 1.092 0.344
## Residuals 44 48575 1104
##
## Call:
## lm(formula = acsf_retain ~ age_current + time_injury_ex, data = df_time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -57.420 -18.947 -5.366 10.466 132.029
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 73.2745 16.5997 4.414 6.71e-05 ***
## age_current -0.3879 0.3464 -1.120 0.269
## time_injury_exMid 7.5033 13.5700 0.553 0.583
## time_injury_exLater 19.5197 12.4025 1.574 0.123
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 33.13 on 43 degrees of freedom
## Multiple R-squared: 0.0743, Adjusted R-squared: 0.009714
## F-statistic: 1.15 on 3 and 43 DF, p-value: 0.3397
This is exploratory and post hoc analysis- likely unable to report to avoid p-hacking, more for information gathering
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 35 17.6 0.039 0.961
## Residuals 44 19638 446.3
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 190 94.92 0.489 0.616
## Residuals 44 8533 193.93
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 322 160.8 0.814 0.45
## Residuals 44 8690 197.5
## Df Sum Sq Mean Sq F value Pr(>F)
## time_injury_ex 2 24 12.14 0.154 0.858
## Residuals 44 3473 78.94
Below is the comparison of t-values for step 3 and step 4 to see if the inclusion of time since injury significantly changed the contribution of each variable in the model. (not sure this is an appropriate way to report in paper, but wanted to see…)
## Variable z_score p_value
## 1 age_current 1.87537056 0.06074176
## 2 phys_health_index -0.65610266 0.51175808
## 3 emo_health_index 0.65707013 0.51113582
## 4 tbiqol_genconcern_tscore 0.03873711 0.96909999
## 5 spstotal -1.06289614 0.28782905
## 6 frsbe_total 1.51617622 0.12947480
Create a sequence from 1 to 44 numbers <- 1:46
Randomly select 8 numbers selected_numbers <- sample(numbers, 8)
Format the selected numbers with leading zeros formatted_numbers <- sprintf(“%03d”, selected_numbers)
Print the selected numbers print(formatted_numbers)
“020” “007” “006” “019” “024” “041” “013” “037”
Social ACS3